In a surveillance system, a camera will image a particular region. Each region has a particular context. By understanding the context, an operator can understand the activities that are occurring there and determine whether an activity is important or not. As a result, a human monitoring video of a region can effectively classify events as being of significance or not. However, such manual monitoring can be extremely tedious, with long stretches of insignificant and/or no activity. As a result, an individual will tend to become inattentive, thereby reducing his/her effectiveness at manually monitoring region(s).
Current automated/semi-automated surveillance solutions attempt to automatically detect and report significant events. In general, a significant event is detected based on motion of an object within the region. Common image-based triggers for the event include motion within a restricted area, occurrence of an event at a particular time, motion that is too fast/slow, and/or the like. Further, an image-based trigger may be based on one or more attributes of the object, such as, for example, the presence of an unauthorized individual. However, to date, these surveillance solutions are susceptible to false alarms.